Detection of the environment of means of locomotion using sensors, and automatic intervention in the control system of the means of locomotion based on the sensor information, are very important in the existing art. It is thereby possible to implement driver assistance systems that, for example, can enhance safety or driving comfort. One such driver assistance system is, for example, the “Home Zone” parking assist system of Robert Bosch GmbH, which can automatically execute a recurrent parking operation based on fixed starting and destination positions. A first prerequisite for this is execution of a manual training run in which the parking assist system detects the necessary environmental information along a movement path (or “trajectory”) of the means of locomotion, evaluates it, and stores it for a subsequent automatic parking operation along that movement path.
In particular, in conjunction with sensor-based driver assistance systems, large data volumes that represent environmental information can occasionally occur when detecting the environmental information. Because driver assistance systems as a rule are implemented in the form of embedded systems, only limited technical resources (memory, computing power, etc.) are often available, since the individual component costs usually need to be kept low due to large production volumes. The available nonvolatile memory, such as a flash memory or EPROM memory, of such an embedded system is therefore in particular often of very small dimensions. In order nevertheless to allow the potentially large data volumes of a sensor-based driver assistance system to be permanently stored, additional measures are therefore necessary so that all the necessary data can be stored in the available memory. Methods for data reduction (lossy) and data compression (lossless) are often used for this purpose. The methods known in the existing art for data reduction and data compression permit a greater or lesser degree of decrease in the original data volume depending on the nature of the data to be detected and stored. In particular when known data reduction methods for image information or moving-image information are used, a data reduction carried out in this manner cannot be sufficient to allow the remaining data to be stored completely in a predefined nonvolatile memory. The reason is that these data reduction methods are directed toward retaining as much of the image information (image resolution, image content, etc.) as possible.
When these image processing methods are used in an embedded system, the result can be either that the image information must be further reduced in an additional processing step (which is generally possible only by additionally discarding possibly relevant image information) or that the nonvolatile memory that is underdimensioned for the specific application must be replaced by a larger memory adapted to the data volume, which would entail higher costs.
In addition to the objective of decreasing the data volume for reasons of memory capacity, a reduced data volume can also be advantageous in terms of data transfer, since a smaller data volume as a rule requires a lower data transfer rate. This can be significant, for example, in the context of data interchange between a processor and an internal or external memory, since cost savings can be achieved here as well thanks to the use of lower-performance (embedded) systems.
An object of the present invention is therefore to optimize the data-reduction and data-compression methods known in the existing art in terms of the storage of feature-based environmental information.